# Physics-Informed Neural Operator for Fast and Scalable Optical Fiber Channel Modelling in Multi-Span Transmission

arXiv: https://arxiv.org/abs/2208.08868 · HTML: https://mlsanity.com/paper/2208.08868/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2022.

## Built on by (most cited first, 3 of 3)

- [Enabling Automatic Differentiation with Mollified Graph Neural Operators](https://mlsanity.com/paper/2504.08277/) (2025)
- [Principle Driven Parameterized Fiber Model based on GPT-PINN Neural Network](https://mlsanity.com/paper/2408.09951/) (2024)
- [Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schr"odinger Equation Using Linear Feature Decoupling Method](https://mlsanity.com/paper/2411.04511/) (2024)

## Abstract (arXiv)

We propose efficient modelling of optical fiber channel via NLSE-constrained physics-informed neural operator without reference solutions. This method can be easily scalable for distance, sequence length, launch power, and signal formats, and is implemented for ultra-fast simulations of 16-QAM signal transmission with ASE noise.